Decision Fusion by Boosting Method for Multi-Modal Voice Activity Detection

Shin'ichi Takeuchi, Takashi Hashiba, Satoshi Tamura, Satoru Hayamizu

研究成果: Paper査読

抄録

In this paper, we propose a multi-modal voice activity detection system (VAD) that uses audio and visual information. In multi-modal (speech) signal processing, there are two methods for fusing the audio and the visual information: concatenating the audio and visual features, and employing audio-only and visual-only classifiers, then fusing the unimodal decisions. We investigate the effectiveness of decision fusion given by the results from AdaBoost. AdaBoost is one of the machine learning method. By using AdaBoost, the effective classifier is constructed by combining weak classifiers. It classifies input data into two classes based on the weighted results from weak classifiers. In proposed method, this fusion scheme is applied to decision fusion of multi-modal VAD. Experimental results show proposed method to generally be more effective.

本文言語English
出版ステータスPublished - 2010
外部発表はい
イベント2010 International Conference on Auditory-Visual Speech Processing, AVSP 2010 - Hakone, Japan
継続期間: 2010 9月 302010 10月 3

Conference

Conference2010 International Conference on Auditory-Visual Speech Processing, AVSP 2010
国/地域Japan
CityHakone
Period10/9/3010/10/3

ASJC Scopus subject areas

  • 言語および言語学
  • 言語聴覚療法
  • 耳鼻咽喉科学

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